2D Categorized Normal Probability Plots

This type of probability plot is constructed
as follows. First, within each category, the values (observations) are
rank ordered. From these ranks, you can compute z
values (i.e., standardized values of the normal distribution) based on
the assumption that the data come from a normal distribution (see Computational
Note). These z values are
plotted on the Y-axis in the plot. If the observed values (plotted on
the X-axis) are normally distributed, all values should fall onto a straight
line. If the values are not normally distributed, they will deviate from
the line. Outliers may also become evident in this plot. If there is a
general lack of fit, and the data seem to form a clear pattern (e.g.,
an S shape) around the line, then the variable may have to be transformed
in some way (e.g., a log transformation to "pull-in" the tail
of the distribution, etc.) before some statistical techniques that are
affected by non-normality can be used.

When you create categorized probability plots,
a series of standard probability plots, one for each category of cases
identified by the X or X and Y category variables (or identified by the
multiple subset criteria) is produced.